This paper adopts the hot list and label system to solve the cold start problem, for the user reaching the product page through search engine, its recommendation is based on the solr full-text IV search engine and the search keywords related to the products. Mining user behavior pattern by mixed mining way, and implicit and explicit information to determine the user preferences, and the construct of user behavior is based on the vector space model. To calculate the keyword weight of product features, this paper use HTTPCWS system of Chinese word segmentation. Under the analysis of the advantages and disadvantages of each recommendation algorithm, combining with the actual data, this article presents combined weighted algorithm to achieve personalized recommendation; In the end this paper realize a personalized product recommendation system based on the platform of People Mall e-commerce.

e-commerce, personalized recommendation system, user behavior patterns